Several applications of networked systems require nodes to have precise knowledge of their geometric position. A common setup is the case in which each node is capable of measuring distances with respect to a subset of mates and has to estimate its position in a given reference frame. Most of the state-of-the-art algorithms for network localization presuppose a central unit, capable of collecting agents' measurements and retrieving the configuration of the whole network. In this paper, we explore a decentralized approach to localization based on a distributed implementation of a gradient method with Barzilai-Borwein stepsizes. The advantage of this approach is that each agent may autonomously compute its position estimate, exchanging information only with its neighbors, without need of communicating with a central station and without needing complete knowledge of the network structure. This decentralized scheme is proved to converge, under an hypothesis of network connectivity, to the same solution as its centralized counterpart. Computational performance and scalability of the described approach are also illustrated through numerical experiments.

A distributed gradient method for localization of formations using relative range measurements / Calafiore, Giuseppe Carlo; Carlone, Luca; Wei, Mingzhu. - STAMPA. - (2010), pp. 1146-1151. (Intervento presentato al convegno The 2010 IEEE Multi-Conference on Systems and Control (MSC’10) tenutosi a Yokohama (Japan) nel September 8-10, 2010) [10.1109/CACSD.2010.5612764].

A distributed gradient method for localization of formations using relative range measurements

CALAFIORE, Giuseppe Carlo;CARLONE, LUCA;WEI, MINGZHU
2010

Abstract

Several applications of networked systems require nodes to have precise knowledge of their geometric position. A common setup is the case in which each node is capable of measuring distances with respect to a subset of mates and has to estimate its position in a given reference frame. Most of the state-of-the-art algorithms for network localization presuppose a central unit, capable of collecting agents' measurements and retrieving the configuration of the whole network. In this paper, we explore a decentralized approach to localization based on a distributed implementation of a gradient method with Barzilai-Borwein stepsizes. The advantage of this approach is that each agent may autonomously compute its position estimate, exchanging information only with its neighbors, without need of communicating with a central station and without needing complete knowledge of the network structure. This decentralized scheme is proved to converge, under an hypothesis of network connectivity, to the same solution as its centralized counterpart. Computational performance and scalability of the described approach are also illustrated through numerical experiments.
2010
9781424453542
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2381229
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo